Chart below provides heatmap of Romad values.

Analysing Romad value on all-time data since 2015, the regions surrounding the following values performed particularly well:

That suggests 22 or 148 could be a good choice for the slow SMA.

Details can be explored using 'Romad Zoming-in' button below.

It might be worth splitting data into smaller 'buckets' and backtesting the strategy individually on each bucket rather than on the entire 7-year period. And then calculating an average value for each combination of parameters.

This method does exactly that. Bucket sizes were decided to be 1 year-long periods of time, where each bucket is overlapping by 6 months. One year period is hopefully long enough not to disadvantage higher values of MVAs as they require more bars to obtain the first value (this is partially also achieved by overlapping). Following are the buckets periods:

• 2015/7 - 2016/6
• 2016/1 - 2016/12
• 2016/7 - 2017/6
• 2017/1 - 2017/12
• 2017/7 - 2018/6
• 2018/1 - 2018/12
• 2018/7 - 2019/6
• 2019/1 - 2019/12
• 2019/7 - 2020/6
• 2020/1 - 2020/12
• 2020/7 - 2021/6
• 2021/1 - 2021/12
• 2021/7 - 2022/6

Chart below allows examination of more detailed data from the darker left bottom region of the above chart.

Not sure if averaging the buckets provides a more statistically significant result, however, the below values are surrounded by best-performing parameter regions according to this type of data:

That suggests 15 could be a good choice for the slow SMA.

15 60 appears to be the best performing parameter settings from this perspective.

Details can be explored using 'Individual Buckets Results' button below. It is interesting to see how the performance changes across individual time buckets.